R Data Visualistion 2 - Box, histogram, and line plots
Andrew Moles
Learning Developer, Digital Skills Lab25 October, 2021
What this workshop will cover
- How to make box plots with ggplot2
- Displaying distributions with histograms
- Working with dates with the lubridate package
- How to make time series line plots
Why this style?
- Online training is tiring so keeping the sessions to one hour
- No or limited demonstrations provided in order to provide more real world experience - you have a problem and you look up how to solve it, adapting example code
- Trainer support to guide through process of learning
We will be working in pairs:
- Option to work together on worksheet or to work individually
- If possible have your camera on and introduce yourself to each other
What to do when getting stuck:
- Ask your team members
- Search online:
- The answer box on the top of Google’s results page
- stackoverflow.com (for task-specific solutions)
- https://www.r-bloggers.com/ (topic based tutorials)
- Don’t struggle too long looking online, ask the trainer if you can’t find a solution!
In this data visualisation workshop we will be building on the concepts learnt in the first workshop of how to construct visualisations using the ggplot2 library.
We will be using one new package called lubridate, a tidyverse package which is designed to make working with dates and times easier; this will help us in making time series visualisations. The the code below to install lubridate.
# install lubridate
install.packages("lubridate")Before we start we will need to load the libraries we will be using during this session. Run the code below to load your libraries.
# libraries we will be using
library(ggplot2)
library(dplyr)
library(lubridate)
library(readr)
library(janitor)
library(RColorBrewer)Box plots
Box plots are designed to compare the differences of a categorical variable (samples or groups). They do this by displaying the summary statistics of a continuous variable (e.g. numeric) for each categorical variable.
The summary statistics shown are:
- The median (middle value)
- Interquartile range, known as IQR, which has values from 25% to 75% (or 25th to 75th percentile)
- First quartile, known as Q1, which has a value of 25%
- Second quartile, known as Q3, which has a value of 75%
- “minimum” value, calculated as
Q1 + 1.5*IQR - “maximum” value, calculated as
Q3 + 1.5*IQR - Outlier, which are values that fall outside of the maximum or minimum values
We will use data from the Pokémon games again for our examples for box plots, which was web scraped from https://pokemondb.net/pokedex/all.
# load and clean names
pokemon <- read_csv("https://raw.githubusercontent.com/andrewmoles2/webScraping/main/R/data/pokemon.csv") %>%
clean_names()
# review data
pokemon %>%
glimpse()## Rows: 952
## Columns: 13
## $ number <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
## $ name <chr> "Bulbasaur", "Ivysaur", "Venusaur", "Charmander", "Charmele…
## $ type1 <chr> "Grass", "Grass", "Grass", "Fire", "Fire", "Fire", "Water",…
## $ type2 <chr> "Poison", "Poison", "Poison", NA, NA, "Flying", NA, NA, NA,…
## $ total <dbl> 318, 405, 525, 309, 405, 534, 314, 405, 530, 195, 205, 395,…
## $ hp <dbl> 45, 60, 80, 39, 58, 78, 44, 59, 79, 45, 50, 60, 40, 45, 65,…
## $ attack <dbl> 49, 62, 82, 52, 64, 84, 48, 63, 83, 30, 20, 45, 35, 25, 90,…
## $ defense <dbl> 49, 63, 83, 43, 58, 78, 65, 80, 100, 35, 55, 50, 30, 50, 40…
## $ sp_atk <dbl> 65, 80, 100, 60, 80, 109, 50, 65, 85, 20, 25, 90, 20, 25, 4…
## $ sp_def <dbl> 65, 80, 100, 50, 65, 85, 64, 80, 105, 20, 25, 80, 20, 25, 8…
## $ speed <dbl> 45, 60, 80, 65, 80, 100, 43, 58, 78, 45, 30, 70, 50, 35, 75…
## $ legendary <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ generation <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…For these examples we will just look at one type of Pokémon, the electric type; the most famous of which is Pikachu! First we extract just the electric type Pokémon, and make relevant columns factors.
# select columns to convert to factor
to_factor <- c("type1", "type2", "generation")
# extract just electric pokemon and make cols factors
electric_pokemon <- pokemon %>%
filter(type1 == "Electric" | type2 == "Electric") %>%
mutate(across(all_of(to_factor), factor))To make a box plot in ggplot we use the geom_boxplot() geom function. One of our axis variables has to be categorical and the other has to be numeric. In the below example we will use generation (categorical) and total (numeric).
# generation by total
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot()
From the output we see a few things. First is that each box has a line through the middle which indicates the median; the box itself is our interquartile range. The lines above and below the boxes (know as whiskers) are the maximum and minimum values. The black dots indicate outliers, which have fallen outside our max and min values.
Just like with scatter and bar plots we can change the colours! You can use either fill or colour arguments with box plots, but fill tends to look better.
We will use the colour of Pikachu to colour our boxes. We used the pokemon colour picker to get the colour of pikachu: https://pokepalettes.com/#pikachu
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652")
Sometimes it is useful to remove the outliers. To do so you add in the outlier.shape = NA argument.
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA)
Displaying outliers is usually a good idea so we will keep them, and change the colour and shape of them. To adjust these we use outlier.colour and outlier.shape. We’ve used the colour of Pikachu’s cheeks as the outlier colour.
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.colour = "#c52018",
outlier.shape = 15)
Box plots exercise
Improving your box plots
The main issue with box plots, in a similar way to bar plots, is they can hide data. We can fix this by adding a scatter plot over the top of the boxes so we can see the full distribution of the data.
When adding in a scatter plot, we won’t need our outlier colours. We will need to remove them using the outlier.shape = NA argument.
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_point()
Some of our data points are overlapping which makes it a little hard to see all the data. We can fix this by changing the position of our points using the position = "jitter" argument. We can also use geom_jitter() which is a short hand for geom_point(position = "jitter"); we will use geom_jitter() as it is less typing.
# change position in geom_point
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_point(position = "jitter")
# using geom_jitter
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_jitter()
We can also add in a colour grouping to our points to make them more meaningful. We add the colour aesthetic to our geom_jitter function. In the example we are colouring our points by if a pokemon is legendary or not.
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_jitter(aes(colour = legendary))
Finally we can change the colours of our points, which in this case we have done manually.
ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_jitter(aes(colour = legendary)) +
scale_colour_manual(values = c("#c52018", "#41414a"))
Now we can add a title and save the plot! When saving the plot we have manually adjusted the width of the plot. You can also change the height.
electric_pokemon_box <- ggplot(electric_pokemon, aes(x = generation, y = total)) +
geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
geom_jitter(aes(colour = legendary)) +
scale_colour_manual(values = c("#c52018", "#41414a")) +
labs(title = "Summary of electric pokemon for each generation") +
theme_bw()
electric_pokemon_box
ggsave("electric_pokemon_box.png", electric_pokemon_box,
width = 5.5)## Saving 5.5 x 5 in imageImproving your box plots exercise
Use covid data for all exercises.
https://github.com/owid/covid-19-data/tree/master/public/data
Vaccine data and explanation of the variables are found here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations
covid_vaccinations <- read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## location = col_character(),
## iso_code = col_character(),
## date = col_date(format = ""),
## total_vaccinations = col_double(),
## people_vaccinated = col_double(),
## people_fully_vaccinated = col_double(),
## total_boosters = col_logical(),
## daily_vaccinations_raw = col_double(),
## daily_vaccinations = col_double(),
## total_vaccinations_per_hundred = col_double(),
## people_vaccinated_per_hundred = col_double(),
## people_fully_vaccinated_per_hundred = col_double(),
## total_boosters_per_hundred = col_logical(),
## daily_vaccinations_per_million = col_double()
## )## Warning: 9467 parsing failures.
## row col expected actual file
## 2929 total_boosters 1/0/T/F/TRUE/FALSE 298841 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
## 2929 total_boosters_per_hundred 1/0/T/F/TRUE/FALSE 0.01 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
## 2930 total_boosters 1/0/T/F/TRUE/FALSE 559415 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
## 2930 total_boosters_per_hundred 1/0/T/F/TRUE/FALSE 0.01 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
## 2931 total_boosters 1/0/T/F/TRUE/FALSE 908597 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
## .... .......................... .................. ...... .......................................................................................................
## See problems(...) for more details.summary(covid_vaccinations)## location iso_code date total_vaccinations
## Length:56689 Length:56689 Min. :2020-12-01 Min. :0.000e+00
## Class :character Class :character 1st Qu.:2021-04-14 1st Qu.:3.493e+05
## Mode :character Mode :character Median :2021-06-18 Median :2.521e+06
## Mean :2021-06-13 Mean :9.540e+07
## 3rd Qu.:2021-08-18 3rd Qu.:1.479e+07
## Max. :2021-10-24 Max. :6.869e+09
## NA's :24421
## people_vaccinated people_fully_vaccinated total_boosters
## Min. :0.000e+00 Min. :1.000e+00 Mode:logical
## 1st Qu.:2.594e+05 1st Qu.:1.429e+05 TRUE:105
## Median :1.740e+06 Median :1.120e+06 NA's:56584
## Mean :5.191e+07 Mean :3.439e+07
## 3rd Qu.:9.190e+06 3rd Qu.:6.256e+06
## Max. :3.832e+09 Max. :2.933e+09
## NA's :25761 NA's :28949
## daily_vaccinations_raw daily_vaccinations total_vaccinations_per_hundred
## Min. : 0 Min. : 0 Min. : 0.00
## 1st Qu.: 6248 1st Qu.: 1079 1st Qu.: 5.99
## Median : 34879 Median : 8749 Median : 32.18
## Mean : 1006825 Mean : 493639 Mean : 49.06
## 3rd Qu.: 227132 3rd Qu.: 60509 3rd Qu.: 84.34
## Max. :56279123 Max. :43391240 Max. :257.34
## NA's :29670 NA's :276 NA's :24421
## people_vaccinated_per_hundred people_fully_vaccinated_per_hundred
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 4.44 1st Qu.: 2.49
## Median : 22.30 Median : 12.71
## Mean : 28.88 Mean : 22.15
## 3rd Qu.: 50.99 3rd Qu.: 38.58
## Max. :120.35 Max. :118.14
## NA's :25761 NA's :28949
## total_boosters_per_hundred daily_vaccinations_per_million
## Mode:logical Min. : 0
## NA's:56689 1st Qu.: 579
## Median : 2258
## Mean : 3488
## 3rd Qu.: 5154
## Max. :117497
## NA's :276high_low_gdp <- c("Sierra Leone", "Madagascar", "Ethiopia","Yemen",
"Zambia", "Nepal", "Switzerland", "Sweden", "Australia",
"Saudi Arabia", "Germany", "United Kingdom")
covid_select_countries <- covid_vaccinations %>%
filter(location %in% high_low_gdp) %>%
mutate(location = factor(location, levels = high_low_gdp))
covid_select_countries %>%
ggplot(aes(location, daily_vaccinations_per_million)) +
geom_boxplot()## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
covid_select_countries %>%
ggplot(aes(location, people_fully_vaccinated)) +
geom_boxplot()## Warning: Removed 1445 rows containing non-finite values (stat_boxplot).
Displaying distributions with histograms
Displaying distributions exercise
Working with the date data type with lubridate
library(dplyr)
library(lubridate)
df <- data.frame(
date = seq(as.Date("2019-01-01"), as.Date("2021-12-01"), by = "days"),
hours_sleep = round(rnorm(1066, mean = 9, sd = 1.5)),
steps = round(rnorm(1066, mean = 8000, sd = 2000))
)
df <- df %>%
mutate(year = year(date),
month = month(date),
day = day(date),
week_day = wday(date, label = TRUE))
df## date hours_sleep steps year month day week_day
## 1 2019-01-01 7 9248 2019 1 1 Tue
## 2 2019-01-02 7 12959 2019 1 2 Wed
## 3 2019-01-03 9 5354 2019 1 3 Thu
## 4 2019-01-04 9 7030 2019 1 4 Fri
## 5 2019-01-05 10 8031 2019 1 5 Sat
## 6 2019-01-06 12 5948 2019 1 6 Sun
## 7 2019-01-07 11 6175 2019 1 7 Mon
## 8 2019-01-08 7 5701 2019 1 8 Tue
## 9 2019-01-09 9 8328 2019 1 9 Wed
## 10 2019-01-10 9 9663 2019 1 10 Thu
## 11 2019-01-11 10 11317 2019 1 11 Fri
## 12 2019-01-12 12 6476 2019 1 12 Sat
## 13 2019-01-13 10 5239 2019 1 13 Sun
## 14 2019-01-14 8 9080 2019 1 14 Mon
## 15 2019-01-15 9 4681 2019 1 15 Tue
## 16 2019-01-16 10 7153 2019 1 16 Wed
## 17 2019-01-17 8 6765 2019 1 17 Thu
## 18 2019-01-18 6 10322 2019 1 18 Fri
## 19 2019-01-19 9 8861 2019 1 19 Sat
## 20 2019-01-20 11 7851 2019 1 20 Sun
## 21 2019-01-21 10 6646 2019 1 21 Mon
## 22 2019-01-22 10 9006 2019 1 22 Tue
## 23 2019-01-23 7 10780 2019 1 23 Wed
## 24 2019-01-24 10 8559 2019 1 24 Thu
## 25 2019-01-25 6 7355 2019 1 25 Fri
## 26 2019-01-26 8 8847 2019 1 26 Sat
## 27 2019-01-27 8 8018 2019 1 27 Sun
## 28 2019-01-28 10 9467 2019 1 28 Mon
## 29 2019-01-29 9 4869 2019 1 29 Tue
## 30 2019-01-30 10 5589 2019 1 30 Wed
## 31 2019-01-31 7 7427 2019 1 31 Thu
## 32 2019-02-01 8 8913 2019 2 1 Fri
## 33 2019-02-02 11 5403 2019 2 2 Sat
## 34 2019-02-03 9 5816 2019 2 3 Sun
## 35 2019-02-04 7 9807 2019 2 4 Mon
## 36 2019-02-05 7 8323 2019 2 5 Tue
## 37 2019-02-06 9 7989 2019 2 6 Wed
## 38 2019-02-07 11 4939 2019 2 7 Thu
## 39 2019-02-08 7 7842 2019 2 8 Fri
## 40 2019-02-09 13 10895 2019 2 9 Sat
## 41 2019-02-10 11 9873 2019 2 10 Sun
## 42 2019-02-11 8 5708 2019 2 11 Mon
## 43 2019-02-12 10 10039 2019 2 12 Tue
## 44 2019-02-13 8 7581 2019 2 13 Wed
## 45 2019-02-14 8 7309 2019 2 14 Thu
## 46 2019-02-15 8 5390 2019 2 15 Fri
## 47 2019-02-16 12 7067 2019 2 16 Sat
## 48 2019-02-17 8 5300 2019 2 17 Sun
## 49 2019-02-18 11 10309 2019 2 18 Mon
## 50 2019-02-19 7 9601 2019 2 19 Tue
## 51 2019-02-20 7 5694 2019 2 20 Wed
## 52 2019-02-21 8 7599 2019 2 21 Thu
## 53 2019-02-22 9 5151 2019 2 22 Fri
## 54 2019-02-23 9 9272 2019 2 23 Sat
## 55 2019-02-24 7 9450 2019 2 24 Sun
## 56 2019-02-25 10 6625 2019 2 25 Mon
## 57 2019-02-26 6 9623 2019 2 26 Tue
## 58 2019-02-27 11 6853 2019 2 27 Wed
## 59 2019-02-28 11 6505 2019 2 28 Thu
## 60 2019-03-01 8 7141 2019 3 1 Fri
## 61 2019-03-02 8 8556 2019 3 2 Sat
## 62 2019-03-03 6 9372 2019 3 3 Sun
## 63 2019-03-04 11 4842 2019 3 4 Mon
## 64 2019-03-05 9 10729 2019 3 5 Tue
## 65 2019-03-06 10 3977 2019 3 6 Wed
## 66 2019-03-07 9 6532 2019 3 7 Thu
## 67 2019-03-08 9 8413 2019 3 8 Fri
## 68 2019-03-09 12 11532 2019 3 9 Sat
## 69 2019-03-10 8 7766 2019 3 10 Sun
## 70 2019-03-11 6 8862 2019 3 11 Mon
## 71 2019-03-12 13 8874 2019 3 12 Tue
## 72 2019-03-13 9 9552 2019 3 13 Wed
## 73 2019-03-14 9 9286 2019 3 14 Thu
## 74 2019-03-15 10 9825 2019 3 15 Fri
## 75 2019-03-16 9 7539 2019 3 16 Sat
## 76 2019-03-17 8 13567 2019 3 17 Sun
## 77 2019-03-18 8 10698 2019 3 18 Mon
## 78 2019-03-19 10 5251 2019 3 19 Tue
## 79 2019-03-20 10 7347 2019 3 20 Wed
## 80 2019-03-21 10 5685 2019 3 21 Thu
## 81 2019-03-22 7 9474 2019 3 22 Fri
## 82 2019-03-23 10 9547 2019 3 23 Sat
## 83 2019-03-24 11 10794 2019 3 24 Sun
## 84 2019-03-25 7 9479 2019 3 25 Mon
## 85 2019-03-26 10 8088 2019 3 26 Tue
## 86 2019-03-27 8 8895 2019 3 27 Wed
## 87 2019-03-28 9 10518 2019 3 28 Thu
## 88 2019-03-29 7 8071 2019 3 29 Fri
## 89 2019-03-30 9 12053 2019 3 30 Sat
## 90 2019-03-31 9 9826 2019 3 31 Sun
## 91 2019-04-01 11 10449 2019 4 1 Mon
## 92 2019-04-02 12 7366 2019 4 2 Tue
## 93 2019-04-03 8 8945 2019 4 3 Wed
## 94 2019-04-04 9 4159 2019 4 4 Thu
## 95 2019-04-05 9 9110 2019 4 5 Fri
## 96 2019-04-06 13 8929 2019 4 6 Sat
## 97 2019-04-07 8 7842 2019 4 7 Sun
## 98 2019-04-08 5 9142 2019 4 8 Mon
## 99 2019-04-09 9 5208 2019 4 9 Tue
## 100 2019-04-10 10 7437 2019 4 10 Wed
## 101 2019-04-11 8 6490 2019 4 11 Thu
## 102 2019-04-12 11 10565 2019 4 12 Fri
## 103 2019-04-13 8 10046 2019 4 13 Sat
## 104 2019-04-14 10 8818 2019 4 14 Sun
## 105 2019-04-15 11 10387 2019 4 15 Mon
## 106 2019-04-16 8 9190 2019 4 16 Tue
## 107 2019-04-17 8 6853 2019 4 17 Wed
## 108 2019-04-18 12 8093 2019 4 18 Thu
## 109 2019-04-19 6 9637 2019 4 19 Fri
## 110 2019-04-20 10 8770 2019 4 20 Sat
## 111 2019-04-21 9 9092 2019 4 21 Sun
## 112 2019-04-22 9 7594 2019 4 22 Mon
## 113 2019-04-23 9 7026 2019 4 23 Tue
## 114 2019-04-24 9 8472 2019 4 24 Wed
## 115 2019-04-25 10 6931 2019 4 25 Thu
## 116 2019-04-26 8 8073 2019 4 26 Fri
## 117 2019-04-27 9 6102 2019 4 27 Sat
## 118 2019-04-28 8 10345 2019 4 28 Sun
## 119 2019-04-29 13 11707 2019 4 29 Mon
## 120 2019-04-30 11 12801 2019 4 30 Tue
## 121 2019-05-01 7 9494 2019 5 1 Wed
## 122 2019-05-02 7 5548 2019 5 2 Thu
## 123 2019-05-03 7 6180 2019 5 3 Fri
## 124 2019-05-04 7 9485 2019 5 4 Sat
## 125 2019-05-05 13 7566 2019 5 5 Sun
## 126 2019-05-06 7 5955 2019 5 6 Mon
## 127 2019-05-07 10 9976 2019 5 7 Tue
## 128 2019-05-08 10 7911 2019 5 8 Wed
## 129 2019-05-09 8 6252 2019 5 9 Thu
## 130 2019-05-10 12 6558 2019 5 10 Fri
## 131 2019-05-11 8 8055 2019 5 11 Sat
## 132 2019-05-12 10 7857 2019 5 12 Sun
## 133 2019-05-13 8 10268 2019 5 13 Mon
## 134 2019-05-14 8 10303 2019 5 14 Tue
## 135 2019-05-15 7 9736 2019 5 15 Wed
## 136 2019-05-16 10 3102 2019 5 16 Thu
## 137 2019-05-17 12 10440 2019 5 17 Fri
## 138 2019-05-18 8 6023 2019 5 18 Sat
## 139 2019-05-19 11 8870 2019 5 19 Sun
## 140 2019-05-20 8 8250 2019 5 20 Mon
## 141 2019-05-21 7 6555 2019 5 21 Tue
## 142 2019-05-22 9 7132 2019 5 22 Wed
## 143 2019-05-23 7 5814 2019 5 23 Thu
## 144 2019-05-24 10 7951 2019 5 24 Fri
## 145 2019-05-25 11 8278 2019 5 25 Sat
## 146 2019-05-26 9 9974 2019 5 26 Sun
## 147 2019-05-27 5 5841 2019 5 27 Mon
## 148 2019-05-28 10 8843 2019 5 28 Tue
## 149 2019-05-29 10 6117 2019 5 29 Wed
## 150 2019-05-30 9 9439 2019 5 30 Thu
## 151 2019-05-31 11 3747 2019 5 31 Fri
## 152 2019-06-01 9 5152 2019 6 1 Sat
## 153 2019-06-02 11 10347 2019 6 2 Sun
## 154 2019-06-03 7 5973 2019 6 3 Mon
## 155 2019-06-04 7 8323 2019 6 4 Tue
## 156 2019-06-05 11 8453 2019 6 5 Wed
## 157 2019-06-06 9 10295 2019 6 6 Thu
## 158 2019-06-07 9 5786 2019 6 7 Fri
## 159 2019-06-08 8 9505 2019 6 8 Sat
## 160 2019-06-09 11 12315 2019 6 9 Sun
## 161 2019-06-10 9 9485 2019 6 10 Mon
## 162 2019-06-11 11 5281 2019 6 11 Tue
## 163 2019-06-12 8 5286 2019 6 12 Wed
## 164 2019-06-13 11 6179 2019 6 13 Thu
## 165 2019-06-14 10 7409 2019 6 14 Fri
## 166 2019-06-15 9 9996 2019 6 15 Sat
## 167 2019-06-16 9 10815 2019 6 16 Sun
## 168 2019-06-17 10 6698 2019 6 17 Mon
## 169 2019-06-18 9 8382 2019 6 18 Tue
## 170 2019-06-19 8 7868 2019 6 19 Wed
## 171 2019-06-20 9 7647 2019 6 20 Thu
## 172 2019-06-21 7 8927 2019 6 21 Fri
## 173 2019-06-22 8 8379 2019 6 22 Sat
## 174 2019-06-23 10 9235 2019 6 23 Sun
## 175 2019-06-24 10 7636 2019 6 24 Mon
## 176 2019-06-25 11 8822 2019 6 25 Tue
## 177 2019-06-26 7 5753 2019 6 26 Wed
## 178 2019-06-27 9 6591 2019 6 27 Thu
## 179 2019-06-28 7 9656 2019 6 28 Fri
## 180 2019-06-29 8 12985 2019 6 29 Sat
## 181 2019-06-30 11 6673 2019 6 30 Sun
## 182 2019-07-01 9 6739 2019 7 1 Mon
## 183 2019-07-02 8 6515 2019 7 2 Tue
## 184 2019-07-03 8 8644 2019 7 3 Wed
## 185 2019-07-04 11 7383 2019 7 4 Thu
## 186 2019-07-05 8 4794 2019 7 5 Fri
## 187 2019-07-06 11 10799 2019 7 6 Sat
## 188 2019-07-07 8 9422 2019 7 7 Sun
## 189 2019-07-08 10 7772 2019 7 8 Mon
## 190 2019-07-09 7 9956 2019 7 9 Tue
## 191 2019-07-10 11 9371 2019 7 10 Wed
## 192 2019-07-11 8 8964 2019 7 11 Thu
## 193 2019-07-12 8 8742 2019 7 12 Fri
## 194 2019-07-13 11 5864 2019 7 13 Sat
## 195 2019-07-14 11 8606 2019 7 14 Sun
## 196 2019-07-15 9 9209 2019 7 15 Mon
## 197 2019-07-16 9 6657 2019 7 16 Tue
## 198 2019-07-17 9 12884 2019 7 17 Wed
## 199 2019-07-18 8 6730 2019 7 18 Thu
## 200 2019-07-19 9 5430 2019 7 19 Fri
## 201 2019-07-20 9 6829 2019 7 20 Sat
## 202 2019-07-21 9 6388 2019 7 21 Sun
## 203 2019-07-22 7 10536 2019 7 22 Mon
## 204 2019-07-23 5 8292 2019 7 23 Tue
## 205 2019-07-24 8 9920 2019 7 24 Wed
## 206 2019-07-25 8 7471 2019 7 25 Thu
## 207 2019-07-26 11 7025 2019 7 26 Fri
## 208 2019-07-27 11 6795 2019 7 27 Sat
## 209 2019-07-28 9 6306 2019 7 28 Sun
## 210 2019-07-29 9 8406 2019 7 29 Mon
## 211 2019-07-30 11 7993 2019 7 30 Tue
## 212 2019-07-31 9 9425 2019 7 31 Wed
## 213 2019-08-01 11 8401 2019 8 1 Thu
## 214 2019-08-02 9 9866 2019 8 2 Fri
## 215 2019-08-03 9 6156 2019 8 3 Sat
## 216 2019-08-04 11 8561 2019 8 4 Sun
## 217 2019-08-05 10 9212 2019 8 5 Mon
## 218 2019-08-06 6 5875 2019 8 6 Tue
## 219 2019-08-07 10 9545 2019 8 7 Wed
## 220 2019-08-08 11 6747 2019 8 8 Thu
## 221 2019-08-09 11 7265 2019 8 9 Fri
## 222 2019-08-10 8 4960 2019 8 10 Sat
## 223 2019-08-11 7 5340 2019 8 11 Sun
## 224 2019-08-12 10 5061 2019 8 12 Mon
## 225 2019-08-13 8 5286 2019 8 13 Tue
## 226 2019-08-14 9 8076 2019 8 14 Wed
## 227 2019-08-15 10 8502 2019 8 15 Thu
## 228 2019-08-16 9 4943 2019 8 16 Fri
## 229 2019-08-17 10 10043 2019 8 17 Sat
## 230 2019-08-18 7 8930 2019 8 18 Sun
## 231 2019-08-19 9 9984 2019 8 19 Mon
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## 735 2021-01-04 10 5725 2021 1 4 Mon
## 736 2021-01-05 11 5463 2021 1 5 Tue
## 737 2021-01-06 11 8013 2021 1 6 Wed
## 738 2021-01-07 7 4646 2021 1 7 Thu
## 739 2021-01-08 7 9948 2021 1 8 Fri
## 740 2021-01-09 10 10011 2021 1 9 Sat
## 741 2021-01-10 5 12358 2021 1 10 Sun
## 742 2021-01-11 9 4569 2021 1 11 Mon
## 743 2021-01-12 9 6656 2021 1 12 Tue
## 744 2021-01-13 10 7807 2021 1 13 Wed
## 745 2021-01-14 10 10583 2021 1 14 Thu
## 746 2021-01-15 9 5788 2021 1 15 Fri
## 747 2021-01-16 10 9372 2021 1 16 Sat
## 748 2021-01-17 11 4913 2021 1 17 Sun
## 749 2021-01-18 8 6716 2021 1 18 Mon
## 750 2021-01-19 8 7624 2021 1 19 Tue
## 751 2021-01-20 7 6328 2021 1 20 Wed
## 752 2021-01-21 9 8315 2021 1 21 Thu
## 753 2021-01-22 7 8083 2021 1 22 Fri
## 754 2021-01-23 8 8118 2021 1 23 Sat
## 755 2021-01-24 7 9888 2021 1 24 Sun
## 756 2021-01-25 9 12085 2021 1 25 Mon
## 757 2021-01-26 8 13065 2021 1 26 Tue
## 758 2021-01-27 7 9041 2021 1 27 Wed
## 759 2021-01-28 9 6946 2021 1 28 Thu
## 760 2021-01-29 8 5627 2021 1 29 Fri
## 761 2021-01-30 8 9607 2021 1 30 Sat
## 762 2021-01-31 8 7126 2021 1 31 Sun
## 763 2021-02-01 11 8441 2021 2 1 Mon
## 764 2021-02-02 8 10877 2021 2 2 Tue
## 765 2021-02-03 9 6383 2021 2 3 Wed
## 766 2021-02-04 6 6561 2021 2 4 Thu
## 767 2021-02-05 6 8884 2021 2 5 Fri
## 768 2021-02-06 10 9086 2021 2 6 Sat
## 769 2021-02-07 8 11323 2021 2 7 Sun
## 770 2021-02-08 10 8174 2021 2 8 Mon
## 771 2021-02-09 10 5932 2021 2 9 Tue
## 772 2021-02-10 11 7861 2021 2 10 Wed
## 773 2021-02-11 11 9569 2021 2 11 Thu
## 774 2021-02-12 7 5136 2021 2 12 Fri
## 775 2021-02-13 8 8808 2021 2 13 Sat
## 776 2021-02-14 9 11794 2021 2 14 Sun
## 777 2021-02-15 8 11823 2021 2 15 Mon
## 778 2021-02-16 7 7832 2021 2 16 Tue
## 779 2021-02-17 9 9157 2021 2 17 Wed
## 780 2021-02-18 9 9595 2021 2 18 Thu
## 781 2021-02-19 10 8205 2021 2 19 Fri
## 782 2021-02-20 8 9906 2021 2 20 Sat
## 783 2021-02-21 10 6355 2021 2 21 Sun
## 784 2021-02-22 9 6405 2021 2 22 Mon
## 785 2021-02-23 11 11741 2021 2 23 Tue
## 786 2021-02-24 11 9554 2021 2 24 Wed
## 787 2021-02-25 11 6547 2021 2 25 Thu
## 788 2021-02-26 8 8499 2021 2 26 Fri
## 789 2021-02-27 8 6001 2021 2 27 Sat
## 790 2021-02-28 10 12707 2021 2 28 Sun
## 791 2021-03-01 10 9492 2021 3 1 Mon
## 792 2021-03-02 10 8331 2021 3 2 Tue
## 793 2021-03-03 9 7510 2021 3 3 Wed
## 794 2021-03-04 9 6455 2021 3 4 Thu
## 795 2021-03-05 10 8369 2021 3 5 Fri
## 796 2021-03-06 10 7357 2021 3 6 Sat
## 797 2021-03-07 9 12423 2021 3 7 Sun
## 798 2021-03-08 10 8774 2021 3 8 Mon
## 799 2021-03-09 8 6984 2021 3 9 Tue
## 800 2021-03-10 10 10119 2021 3 10 Wed
## 801 2021-03-11 9 8309 2021 3 11 Thu
## 802 2021-03-12 10 5704 2021 3 12 Fri
## 803 2021-03-13 8 9818 2021 3 13 Sat
## 804 2021-03-14 7 5681 2021 3 14 Sun
## 805 2021-03-15 10 8433 2021 3 15 Mon
## 806 2021-03-16 8 8898 2021 3 16 Tue
## 807 2021-03-17 7 7729 2021 3 17 Wed
## 808 2021-03-18 8 5716 2021 3 18 Thu
## 809 2021-03-19 10 5827 2021 3 19 Fri
## 810 2021-03-20 9 7793 2021 3 20 Sat
## 811 2021-03-21 10 9744 2021 3 21 Sun
## 812 2021-03-22 10 6515 2021 3 22 Mon
## 813 2021-03-23 12 8888 2021 3 23 Tue
## 814 2021-03-24 9 7978 2021 3 24 Wed
## 815 2021-03-25 8 12051 2021 3 25 Thu
## 816 2021-03-26 9 9406 2021 3 26 Fri
## 817 2021-03-27 7 10821 2021 3 27 Sat
## 818 2021-03-28 10 6625 2021 3 28 Sun
## 819 2021-03-29 10 5847 2021 3 29 Mon
## 820 2021-03-30 10 8389 2021 3 30 Tue
## 821 2021-03-31 9 7633 2021 3 31 Wed
## 822 2021-04-01 11 10046 2021 4 1 Thu
## 823 2021-04-02 7 2664 2021 4 2 Fri
## 824 2021-04-03 7 9289 2021 4 3 Sat
## 825 2021-04-04 10 7817 2021 4 4 Sun
## 826 2021-04-05 13 8292 2021 4 5 Mon
## 827 2021-04-06 10 5240 2021 4 6 Tue
## 828 2021-04-07 9 5790 2021 4 7 Wed
## 829 2021-04-08 8 8871 2021 4 8 Thu
## 830 2021-04-09 8 12131 2021 4 9 Fri
## 831 2021-04-10 12 4485 2021 4 10 Sat
## 832 2021-04-11 8 9958 2021 4 11 Sun
## 833 2021-04-12 8 10042 2021 4 12 Mon
## 834 2021-04-13 7 5027 2021 4 13 Tue
## 835 2021-04-14 6 10117 2021 4 14 Wed
## 836 2021-04-15 12 10462 2021 4 15 Thu
## 837 2021-04-16 9 6825 2021 4 16 Fri
## 838 2021-04-17 8 7796 2021 4 17 Sat
## 839 2021-04-18 9 8626 2021 4 18 Sun
## 840 2021-04-19 10 8287 2021 4 19 Mon
## 841 2021-04-20 8 4231 2021 4 20 Tue
## 842 2021-04-21 8 9359 2021 4 21 Wed
## 843 2021-04-22 8 8354 2021 4 22 Thu
## 844 2021-04-23 7 10126 2021 4 23 Fri
## 845 2021-04-24 6 5156 2021 4 24 Sat
## 846 2021-04-25 6 6383 2021 4 25 Sun
## 847 2021-04-26 10 9341 2021 4 26 Mon
## 848 2021-04-27 7 5127 2021 4 27 Tue
## 849 2021-04-28 8 6613 2021 4 28 Wed
## 850 2021-04-29 11 6964 2021 4 29 Thu
## 851 2021-04-30 11 9395 2021 4 30 Fri
## 852 2021-05-01 8 7829 2021 5 1 Sat
## 853 2021-05-02 11 9672 2021 5 2 Sun
## 854 2021-05-03 7 8183 2021 5 3 Mon
## 855 2021-05-04 7 9683 2021 5 4 Tue
## 856 2021-05-05 10 10842 2021 5 5 Wed
## 857 2021-05-06 12 10211 2021 5 6 Thu
## 858 2021-05-07 10 7508 2021 5 7 Fri
## 859 2021-05-08 9 11005 2021 5 8 Sat
## 860 2021-05-09 7 5903 2021 5 9 Sun
## 861 2021-05-10 8 8161 2021 5 10 Mon
## 862 2021-05-11 11 7512 2021 5 11 Tue
## 863 2021-05-12 7 6513 2021 5 12 Wed
## 864 2021-05-13 9 6666 2021 5 13 Thu
## 865 2021-05-14 8 7692 2021 5 14 Fri
## 866 2021-05-15 9 7787 2021 5 15 Sat
## 867 2021-05-16 10 6268 2021 5 16 Sun
## 868 2021-05-17 9 10807 2021 5 17 Mon
## 869 2021-05-18 9 8400 2021 5 18 Tue
## 870 2021-05-19 10 7917 2021 5 19 Wed
## 871 2021-05-20 9 10771 2021 5 20 Thu
## 872 2021-05-21 6 11651 2021 5 21 Fri
## 873 2021-05-22 11 9809 2021 5 22 Sat
## 874 2021-05-23 9 9170 2021 5 23 Sun
## 875 2021-05-24 8 5710 2021 5 24 Mon
## 876 2021-05-25 7 6608 2021 5 25 Tue
## 877 2021-05-26 12 7669 2021 5 26 Wed
## 878 2021-05-27 8 6726 2021 5 27 Thu
## 879 2021-05-28 10 6057 2021 5 28 Fri
## 880 2021-05-29 11 4946 2021 5 29 Sat
## 881 2021-05-30 8 7030 2021 5 30 Sun
## 882 2021-05-31 11 7837 2021 5 31 Mon
## 883 2021-06-01 9 6743 2021 6 1 Tue
## 884 2021-06-02 11 7196 2021 6 2 Wed
## 885 2021-06-03 7 9236 2021 6 3 Thu
## 886 2021-06-04 9 7220 2021 6 4 Fri
## 887 2021-06-05 11 6911 2021 6 5 Sat
## 888 2021-06-06 9 7096 2021 6 6 Sun
## 889 2021-06-07 7 4960 2021 6 7 Mon
## 890 2021-06-08 8 7190 2021 6 8 Tue
## 891 2021-06-09 10 3647 2021 6 9 Wed
## 892 2021-06-10 10 5836 2021 6 10 Thu
## 893 2021-06-11 12 7181 2021 6 11 Fri
## 894 2021-06-12 9 6249 2021 6 12 Sat
## 895 2021-06-13 10 7155 2021 6 13 Sun
## 896 2021-06-14 8 8213 2021 6 14 Mon
## 897 2021-06-15 10 8000 2021 6 15 Tue
## 898 2021-06-16 8 8143 2021 6 16 Wed
## 899 2021-06-17 10 9792 2021 6 17 Thu
## 900 2021-06-18 10 7114 2021 6 18 Fri
## 901 2021-06-19 10 6435 2021 6 19 Sat
## 902 2021-06-20 8 8946 2021 6 20 Sun
## 903 2021-06-21 7 10293 2021 6 21 Mon
## 904 2021-06-22 8 8444 2021 6 22 Tue
## 905 2021-06-23 10 8177 2021 6 23 Wed
## 906 2021-06-24 12 10125 2021 6 24 Thu
## 907 2021-06-25 9 5784 2021 6 25 Fri
## 908 2021-06-26 8 9769 2021 6 26 Sat
## 909 2021-06-27 9 6381 2021 6 27 Sun
## 910 2021-06-28 5 8771 2021 6 28 Mon
## 911 2021-06-29 9 10414 2021 6 29 Tue
## 912 2021-06-30 10 9610 2021 6 30 Wed
## 913 2021-07-01 10 7398 2021 7 1 Thu
## 914 2021-07-02 11 9877 2021 7 2 Fri
## 915 2021-07-03 7 5189 2021 7 3 Sat
## 916 2021-07-04 9 8662 2021 7 4 Sun
## 917 2021-07-05 10 5634 2021 7 5 Mon
## 918 2021-07-06 7 11196 2021 7 6 Tue
## 919 2021-07-07 12 5974 2021 7 7 Wed
## 920 2021-07-08 6 9577 2021 7 8 Thu
## 921 2021-07-09 8 9345 2021 7 9 Fri
## 922 2021-07-10 9 10188 2021 7 10 Sat
## 923 2021-07-11 8 8241 2021 7 11 Sun
## 924 2021-07-12 8 6960 2021 7 12 Mon
## 925 2021-07-13 10 6158 2021 7 13 Tue
## 926 2021-07-14 10 9172 2021 7 14 Wed
## 927 2021-07-15 9 6170 2021 7 15 Thu
## 928 2021-07-16 10 5910 2021 7 16 Fri
## 929 2021-07-17 11 6548 2021 7 17 Sat
## 930 2021-07-18 7 7315 2021 7 18 Sun
## 931 2021-07-19 11 10082 2021 7 19 Mon
## 932 2021-07-20 10 10030 2021 7 20 Tue
## 933 2021-07-21 10 7906 2021 7 21 Wed
## 934 2021-07-22 7 7810 2021 7 22 Thu
## 935 2021-07-23 9 7580 2021 7 23 Fri
## 936 2021-07-24 7 8114 2021 7 24 Sat
## 937 2021-07-25 11 9740 2021 7 25 Sun
## 938 2021-07-26 13 9460 2021 7 26 Mon
## 939 2021-07-27 8 7694 2021 7 27 Tue
## 940 2021-07-28 9 4731 2021 7 28 Wed
## 941 2021-07-29 8 5279 2021 7 29 Thu
## 942 2021-07-30 11 6402 2021 7 30 Fri
## 943 2021-07-31 12 7487 2021 7 31 Sat
## 944 2021-08-01 8 7725 2021 8 1 Sun
## 945 2021-08-02 7 10943 2021 8 2 Mon
## 946 2021-08-03 10 3821 2021 8 3 Tue
## 947 2021-08-04 10 10862 2021 8 4 Wed
## 948 2021-08-05 8 9955 2021 8 5 Thu
## 949 2021-08-06 6 7185 2021 8 6 Fri
## 950 2021-08-07 8 6030 2021 8 7 Sat
## 951 2021-08-08 10 11233 2021 8 8 Sun
## 952 2021-08-09 8 6051 2021 8 9 Mon
## 953 2021-08-10 8 8802 2021 8 10 Tue
## 954 2021-08-11 11 13862 2021 8 11 Wed
## 955 2021-08-12 8 10239 2021 8 12 Thu
## 956 2021-08-13 9 8180 2021 8 13 Fri
## 957 2021-08-14 6 8899 2021 8 14 Sat
## 958 2021-08-15 8 11030 2021 8 15 Sun
## 959 2021-08-16 11 6186 2021 8 16 Mon
## 960 2021-08-17 8 4938 2021 8 17 Tue
## 961 2021-08-18 11 5047 2021 8 18 Wed
## 962 2021-08-19 7 6572 2021 8 19 Thu
## 963 2021-08-20 10 7587 2021 8 20 Fri
## 964 2021-08-21 9 10789 2021 8 21 Sat
## 965 2021-08-22 10 12178 2021 8 22 Sun
## 966 2021-08-23 10 11861 2021 8 23 Mon
## 967 2021-08-24 8 6106 2021 8 24 Tue
## 968 2021-08-25 7 8907 2021 8 25 Wed
## 969 2021-08-26 10 10946 2021 8 26 Thu
## 970 2021-08-27 10 8072 2021 8 27 Fri
## 971 2021-08-28 9 7038 2021 8 28 Sat
## 972 2021-08-29 10 9323 2021 8 29 Sun
## 973 2021-08-30 11 3617 2021 8 30 Mon
## 974 2021-08-31 10 6687 2021 8 31 Tue
## 975 2021-09-01 10 8699 2021 9 1 Wed
## 976 2021-09-02 6 8146 2021 9 2 Thu
## 977 2021-09-03 9 11033 2021 9 3 Fri
## 978 2021-09-04 10 6300 2021 9 4 Sat
## 979 2021-09-05 7 8084 2021 9 5 Sun
## 980 2021-09-06 9 10272 2021 9 6 Mon
## 981 2021-09-07 11 8335 2021 9 7 Tue
## 982 2021-09-08 9 6602 2021 9 8 Wed
## 983 2021-09-09 8 9800 2021 9 9 Thu
## 984 2021-09-10 6 5302 2021 9 10 Fri
## 985 2021-09-11 10 3124 2021 9 11 Sat
## 986 2021-09-12 10 7499 2021 9 12 Sun
## 987 2021-09-13 9 3261 2021 9 13 Mon
## 988 2021-09-14 7 7909 2021 9 14 Tue
## 989 2021-09-15 8 6558 2021 9 15 Wed
## 990 2021-09-16 9 8096 2021 9 16 Thu
## 991 2021-09-17 7 10736 2021 9 17 Fri
## 992 2021-09-18 11 7718 2021 9 18 Sat
## 993 2021-09-19 8 11017 2021 9 19 Sun
## 994 2021-09-20 8 7331 2021 9 20 Mon
## 995 2021-09-21 10 7711 2021 9 21 Tue
## 996 2021-09-22 9 10268 2021 9 22 Wed
## 997 2021-09-23 10 12481 2021 9 23 Thu
## 998 2021-09-24 10 8185 2021 9 24 Fri
## 999 2021-09-25 9 6904 2021 9 25 Sat
## 1000 2021-09-26 8 10017 2021 9 26 Sun
## 1001 2021-09-27 10 9493 2021 9 27 Mon
## 1002 2021-09-28 8 8983 2021 9 28 Tue
## 1003 2021-09-29 9 5180 2021 9 29 Wed
## 1004 2021-09-30 10 6151 2021 9 30 Thu
## 1005 2021-10-01 10 5084 2021 10 1 Fri
## 1006 2021-10-02 8 7758 2021 10 2 Sat
## 1007 2021-10-03 11 10819 2021 10 3 Sun
## 1008 2021-10-04 9 8807 2021 10 4 Mon
## 1009 2021-10-05 9 7866 2021 10 5 Tue
## 1010 2021-10-06 9 11140 2021 10 6 Wed
## 1011 2021-10-07 8 7207 2021 10 7 Thu
## 1012 2021-10-08 7 9786 2021 10 8 Fri
## 1013 2021-10-09 6 8024 2021 10 9 Sat
## 1014 2021-10-10 9 7544 2021 10 10 Sun
## 1015 2021-10-11 10 9347 2021 10 11 Mon
## 1016 2021-10-12 10 9881 2021 10 12 Tue
## 1017 2021-10-13 12 7146 2021 10 13 Wed
## 1018 2021-10-14 12 7166 2021 10 14 Thu
## 1019 2021-10-15 10 4632 2021 10 15 Fri
## 1020 2021-10-16 8 6262 2021 10 16 Sat
## 1021 2021-10-17 11 6517 2021 10 17 Sun
## 1022 2021-10-18 8 5179 2021 10 18 Mon
## 1023 2021-10-19 8 7729 2021 10 19 Tue
## 1024 2021-10-20 11 6604 2021 10 20 Wed
## 1025 2021-10-21 10 4555 2021 10 21 Thu
## 1026 2021-10-22 10 7689 2021 10 22 Fri
## 1027 2021-10-23 9 7198 2021 10 23 Sat
## 1028 2021-10-24 7 10083 2021 10 24 Sun
## 1029 2021-10-25 8 7791 2021 10 25 Mon
## 1030 2021-10-26 8 8152 2021 10 26 Tue
## 1031 2021-10-27 7 11270 2021 10 27 Wed
## 1032 2021-10-28 8 5527 2021 10 28 Thu
## 1033 2021-10-29 10 7255 2021 10 29 Fri
## 1034 2021-10-30 10 6218 2021 10 30 Sat
## 1035 2021-10-31 11 10446 2021 10 31 Sun
## 1036 2021-11-01 11 9287 2021 11 1 Mon
## 1037 2021-11-02 11 5942 2021 11 2 Tue
## 1038 2021-11-03 11 11499 2021 11 3 Wed
## 1039 2021-11-04 9 9097 2021 11 4 Thu
## 1040 2021-11-05 8 7513 2021 11 5 Fri
## 1041 2021-11-06 10 8104 2021 11 6 Sat
## 1042 2021-11-07 6 9249 2021 11 7 Sun
## 1043 2021-11-08 8 8798 2021 11 8 Mon
## 1044 2021-11-09 9 8043 2021 11 9 Tue
## 1045 2021-11-10 10 8379 2021 11 10 Wed
## 1046 2021-11-11 12 5384 2021 11 11 Thu
## 1047 2021-11-12 8 8862 2021 11 12 Fri
## 1048 2021-11-13 10 8681 2021 11 13 Sat
## 1049 2021-11-14 10 12078 2021 11 14 Sun
## 1050 2021-11-15 8 9691 2021 11 15 Mon
## 1051 2021-11-16 9 10516 2021 11 16 Tue
## 1052 2021-11-17 7 5953 2021 11 17 Wed
## 1053 2021-11-18 8 9771 2021 11 18 Thu
## 1054 2021-11-19 9 6108 2021 11 19 Fri
## 1055 2021-11-20 9 9608 2021 11 20 Sat
## 1056 2021-11-21 8 6784 2021 11 21 Sun
## 1057 2021-11-22 9 9104 2021 11 22 Mon
## 1058 2021-11-23 7 11249 2021 11 23 Tue
## 1059 2021-11-24 6 8232 2021 11 24 Wed
## 1060 2021-11-25 8 12374 2021 11 25 Thu
## 1061 2021-11-26 9 7699 2021 11 26 Fri
## 1062 2021-11-27 10 8790 2021 11 27 Sat
## 1063 2021-11-28 9 11433 2021 11 28 Sun
## 1064 2021-11-29 9 8351 2021 11 29 Mon
## 1065 2021-11-30 8 9454 2021 11 30 Tue
## 1066 2021-12-01 9 7313 2021 12 1 Weddf %>%
count(hours_sleep)## hours_sleep n
## 1 5 11
## 2 6 45
## 3 7 126
## 4 8 238
## 5 9 236
## 6 10 231
## 7 11 134
## 8 12 35
## 9 13 10df %>%
ggplot(aes(x = week_day, y = hours_sleep)) +
geom_point() +
stat_summary(fun = mean, geom = "point",
shape = 95, size = 20)
df %>%
ggplot(aes(x = week_day, y = steps)) +
geom_point() +
stat_summary(fun = mean, geom = "point",
shape = 95, size = 20)
lubridate exercise
lubridate::month(covid_select_countries$date)## [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
## [25] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4
## [49] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [73] 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [97] 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6
## [121] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7
## [145] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [169] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [193] 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [217] 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10
## [241] 10 10 10 10 10 10 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4
## [265] 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5
## [289] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6
## [313] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [337] 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [361] 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [385] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9
## [409] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10
## [433] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 12 12 12 12 12
## [457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [481] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [505] 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [529] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4
## [553] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [577] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [601] 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [625] 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7
## [649] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8
## [673] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [697] 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [721] 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [745] 10 10 10 10 10 10 10 10 10 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [769] 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [793] 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7
## [817] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8
## [841] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [865] 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [889] 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [913] 10 10 10 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [937] 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
## [961] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4
## [985] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1009] 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1033] 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [1057] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7
## [1081] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8
## [1105] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [1129] 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [1153] 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1177] 10 10 10 10 10 10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1201] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1225] 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3
## [1249] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4
## [1273] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1297] 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1321] 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [1345] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7
## [1369] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8
## [1393] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [1417] 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [1441] 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1465] 10 10 10 10 10 10 10 10 10 10 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1489] 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [1513] 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1537] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6
## [1561] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7
## [1585] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [1609] 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [1633] 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9
## [1657] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10
## [1681] 10 10 10 10 10 10 10 10 10 10 10 10 10 12 12 12 12 12 1 1 1 1 1 1
## [1705] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1729] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1753] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1777] 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
## [1801] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5
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## [1801] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6
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## [1849] 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [1873] 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [1897] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10
## [1921] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3
## [1945] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [1969] 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## [1993] 22 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13
## [2017] 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6
## [2041] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2
## [2065] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## [2089] 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## [2113] 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13
## [2137] 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6
## [2161] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## [2185] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## [2209] 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [2233] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10
## [2257] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4
## [2281] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 10 11 12 13 14 15 16
## [2305] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9
## [2329] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5
## [2353] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## [2377] 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## [2401] 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## [2425] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9
## [2449] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3
## [2473] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [2497] 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## [2521] 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13
## [2545] 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7
## [2569] 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 9 10 11 12 13 14 15 16
## [2593] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9
## [2617] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3
## [2641] 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [2665] 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## [2689] 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13
## [2713] 14 15 16 17 18 19 20 21 22 23 24 25 26 27 14 15 16 17 18 19 20 21 22 23
## [2737] 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [2761] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10
## [2785] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4
## [2809] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
## [2833] 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## [2857] 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## [2881] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8
## [2905] 9 10 11 12 13 14 15 16 17 18 19 20 21 22Time series plots
Time series plots exercise
Ideas
https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/
https://www.cedricscherer.com/2019/05/17/the-evolution-of-a-ggplot-ep.-1/
# annotate plot to show where Pikachu is
electric_pokemon %>%
ggplot(aes(x = as.numeric(generation), y = total)) +
geom_point() +
annotate(geom = "curve", x = 3, y = 230, xend = 1.1, yend = 320,
curvature = 0.15, arrow = arrow(length = unit(2, "mm"))) +
annotate(geom = "text", x = 3, y = 230,
label = "Pikachu", hjust = "left") +
annotate(geom = "point", x = 1, y = 320, colour = "Yellow", size = 3)
---
title: "R Data Visualistion 2 - Box, histogram, and line plots"
author:
   - name: Andrew Moles
     affiliation: Learning Developer, Digital Skills Lab
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_document: 
    theme: readable
    highlight: pygments
    keep_md: no
    code_download: true
    toc: TRUE
    toc_float: TRUE
---

# What this workshop will cover

-   How to make box plots with ggplot2
-   Displaying distributions with histograms
-   Working with dates with the lubridate package
-   How to make time series line plots

## Why this style?

-   Online training is tiring so keeping the sessions to one hour
-   No or limited demonstrations provided in order to provide more real world experience - you have a problem and you look up how to solve it, adapting example code
-   Trainer support to guide through process of learning

## We will be working in pairs:

-   Option to work together on worksheet or to work individually
-   If possible have your camera on and introduce yourself to each other

## What to do when getting stuck:

1)  Ask your team members
2)  Search online:

-   The answer box on the top of Google's results page
-   stackoverflow.com (for task-specific solutions)
-   <https://www.r-bloggers.com/> (topic based tutorials)

3)  Don't struggle too long looking online, ask the trainer if you can't find a solution!

------------------------------------------------------------------------

In this data visualisation workshop we will be building on the concepts learnt in the first workshop of how to construct visualisations using the `ggplot2` library. 

![](https://github.com/andrewmoles2/rTrainIntroduction/blob/main/r-data-visualisation-1/images/ggplot2_masterpiece.png?raw=true){width="541"}

We will be using one new package called *lubridate*, a tidyverse package which is designed to make working with dates and times easier; this will help us in making time series visualisations. **The the code below to install lubridate**. 

```{r eval=FALSE}
# install lubridate
install.packages("lubridate")
```

Before we start we will need to load the libraries we will be using during this session. **Run the code below to load your libraries**. 

```{r message=FALSE, warning=FALSE}
# libraries we will be using
library(ggplot2)
library(dplyr)
library(lubridate)
library(readr)
library(janitor)
library(RColorBrewer)
```

# Box plots

Box plots are designed to compare the differences of a categorical variable (samples or groups). They do this by displaying the summary statistics of a continuous variable (e.g. numeric) for each categorical variable. 

The summary statistics shown are: 

- The median (middle value)
- Interquartile range, known as IQR, which has values from 25% to 75% (or 25th to 75th percentile)
- First quartile, known as Q1, which has a value of 25%
- Second quartile, known as Q3, which has a value of 75%
- "minimum" value, calculated as `Q1 + 1.5*IQR`
- "maximum" value, calculated as `Q3 + 1.5*IQR`
- Outlier, which are values that fall outside of the maximum or minimum values

We will use data from the Pokémon games again for our examples for box plots, which was web scraped from <https://pokemondb.net/pokedex/all>.

```{r message=FALSE}
# load and clean names
pokemon <- read_csv("https://raw.githubusercontent.com/andrewmoles2/webScraping/main/R/data/pokemon.csv") %>%
  clean_names()
# review data
pokemon %>%
  glimpse()
```

For these examples we will just look at one type of Pokémon, the electric type; the most famous of which is Pikachu! First we extract just the electric type Pokémon, and make relevant columns factors. 

```{r}
# select columns to convert to factor
to_factor <- c("type1", "type2", "generation")

# extract just electric pokemon and make cols factors
electric_pokemon <- pokemon %>%
  filter(type1 == "Electric" | type2 == "Electric") %>%
  mutate(across(all_of(to_factor), factor))
```

To make a box plot in ggplot we use the `geom_boxplot()` geom function. One of our axis variables has to be categorical and the other has to be numeric. In the below example we will use generation (categorical) and total (numeric). 

```{r}
# generation by total
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot()
```

From the output we see a few things. First is that each box has a line through the middle which indicates the median; the box itself is our interquartile range. The lines above and below the boxes (know as whiskers) are the maximum and minimum values. The black dots indicate outliers, which have fallen outside our max and min values. 

Just like with scatter and bar plots we can change the colours! You can use either fill or colour arguments with box plots, but fill tends to look better. 

We will use the colour of Pikachu to colour our boxes. We used the pokemon colour picker to get the colour of pikachu: <https://pokepalettes.com/#pikachu>

```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652")
```

Sometimes it is useful to remove the outliers. To do so you add in the `outlier.shape = NA` argument. 
```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA)
```

Displaying outliers is usually a good idea so we will keep them, and change the colour and shape of them. To adjust these we use `outlier.colour` and `outlier.shape`. We've used the colour of Pikachu's cheeks as the outlier colour. 
```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.colour = "#c52018",
               outlier.shape = 15)
```

## Box plots exercise 


# Improving your box plots

The main issue with box plots, in a similar way to bar plots, is they can hide data. We can fix this by adding a scatter plot over the top of the boxes so we can see the full distribution of the data. 

When adding in a scatter plot, we won't need our outlier colours. We will need to remove them using the `outlier.shape = NA` argument. 

```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_point()
```

Some of our data points are overlapping which makes it a little hard to see all the data. We can fix this by changing the position of our points using the `position = "jitter"` argument. We can also use `geom_jitter()` which is a short hand for `geom_point(position = "jitter")`; we will use `geom_jitter()` as it is less typing. 

```{r}
# change position in geom_point
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_point(position = "jitter")

# using geom_jitter
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_jitter()
```

We can also add in a colour grouping to our points to make them more meaningful. We add the colour aesthetic to our `geom_jitter` function. In the example we are colouring our points by if a pokemon is legendary or not. 

```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_jitter(aes(colour = legendary))
```

Finally we can change the colours of our points, which in this case we have done manually. 

```{r}
ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_jitter(aes(colour = legendary)) +
  scale_colour_manual(values = c("#c52018", "#41414a"))
```

Now we can add a title and save the plot! When saving the plot we have manually adjusted the width of the plot. You can also change the height. 

```{r}
electric_pokemon_box <- ggplot(electric_pokemon, aes(x = generation, y = total)) +
  geom_boxplot(fill = "#f6e652", outlier.shape = NA) +
  geom_jitter(aes(colour = legendary)) +
  scale_colour_manual(values = c("#c52018", "#41414a")) +
  labs(title = "Summary of electric pokemon for each generation") +
  theme_bw()

electric_pokemon_box

ggsave("electric_pokemon_box.png", electric_pokemon_box,
       width = 5.5)
```


## Improving your box plots exercise

Use covid data for all exercises.

<https://github.com/owid/covid-19-data/tree/master/public/data>

Vaccine data and explanation of the variables are found here: <https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations>

```{r}
covid_vaccinations <- read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv")

summary(covid_vaccinations)

high_low_gdp <- c("Sierra Leone", "Madagascar", "Ethiopia","Yemen", 
                  "Zambia", "Nepal", "Switzerland", "Sweden", "Australia",
                  "Saudi Arabia", "Germany", "United Kingdom")

covid_select_countries <- covid_vaccinations %>%
  filter(location %in% high_low_gdp) %>%
  mutate(location = factor(location, levels = high_low_gdp))

covid_select_countries %>%
  ggplot(aes(location, daily_vaccinations_per_million)) +
  geom_boxplot()

covid_select_countries %>%
  ggplot(aes(location, people_fully_vaccinated)) +
  geom_boxplot()
```



# Displaying distributions with histograms

## Displaying distributions exercise

# Working with the date data type with lubridate

```{r}
library(dplyr)
library(lubridate)

df <- data.frame(
  date = seq(as.Date("2019-01-01"), as.Date("2021-12-01"), by = "days"),
  hours_sleep = round(rnorm(1066, mean = 9, sd = 1.5)),
  steps = round(rnorm(1066, mean = 8000, sd = 2000))
)

df <- df %>%
  mutate(year = year(date),
         month = month(date),
         day = day(date),
         week_day = wday(date, label = TRUE))

df

df %>%
  count(hours_sleep)

df %>%
  ggplot(aes(x = week_day, y = hours_sleep)) +
  geom_point() +
  stat_summary(fun = mean, geom = "point",
               shape = 95, size = 20)


df %>%
  ggplot(aes(x = week_day, y = steps)) + 
  geom_point() +
  stat_summary(fun = mean, geom = "point",
               shape = 95, size = 20)
```

## lubridate exercise

```{r}
lubridate::month(covid_select_countries$date)
lubridate::year(covid_select_countries$date)
lubridate::day(covid_select_countries$date)
```


# Time series plots

## Time series plots exercise

# Ideas

<https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/>

<https://www.cedricscherer.com/2019/05/17/the-evolution-of-a-ggplot-ep.-1/>

```{r}
# annotate plot to show where Pikachu is
electric_pokemon %>%
  ggplot(aes(x = as.numeric(generation), y = total)) +
  geom_point() +
  annotate(geom = "curve", x = 3, y = 230, xend = 1.1, yend = 320,
           curvature = 0.15, arrow = arrow(length = unit(2, "mm"))) +
  annotate(geom = "text", x = 3, y = 230, 
           label = "Pikachu", hjust = "left") +
  annotate(geom = "point", x = 1, y = 320, colour = "Yellow", size = 3)
```
